In the United States, power outages and power quality problems annually cost hundreds of billions each year. Being motivated by this concern, one of the defining characteristics of the emerging smart grid is to support more stable and higher-quality power supply by using information technology.
To assess power quality (PQ), it is a common practice to monitor the quality of voltage and current waveforms by analyzing samples acquired by sensors installed in the power distribution networks.
In contrast with the sinusoidal power waveform generated by electric utilities, power waveforms over transmission lines and at consumer equipment are often distorted. Generally, distortions can be classified a PQ variations and PQ events. While PQ variations are characterized by small and gradual deviations from the sinusoidal voltage and current waveforms, PQ events incur large waveform deviations. PQ events are more detrimental to the power distribution network because the events can potentially inflict severe damages to the electrical equipment and injuries the consumers. Consequently, the occurrence of the PQ events has to be detected accurately and timely to allow appropriate actions.
In practice, PQ event monitoring includes detection and classification. During detection, a PQ event is declared when the waveform distortion exceeds a pre-defined threshold. Then, the distorted waveform can be classified to identify the cause of the PQ event before further analysis is performed.
Three primary PQ event detection methods are known. The first method tracks the root mean squared (RMS) value of the voltage waveform over a moving time window. The likelihood of the occurrence of the PQ event is evaluated based on the RMS change across windows. The RMS-based method is effective in detecting amplitude-related distortions. The second method detects the distortion in the frequency domain by transforming the time waveform into a frequency waveform using either wavelets or a short-time Fourier transform (STFT). The third method decomposes the waveform into a sum of damped sinusoids using super-resolution spectral analysis techniques, such as signal estimation via rotational invariance techniques, or multiple signal classification. The distorted waveform is detected by comparing the decomposed frequency-domain components of a monitored waveform with those of the normal waveform.
The moving time window segments the waveform into blocks before any transformation or decomposition is applied. Therefore, the time resolution of all three conventional methods is restricted by the size of the moving window.
If the window size is sufficiently large to meet the detection rate and the false alarm rate requirements, the delay in detecting the PQ window is increased, and real-time detection is not possible.
The invention corrects this problem.